Efficacy of Popular Diets Applied by Endurance Athletes on Sports Performance: Beneficial or Detrimental? A Narrative Review
Bibliographic record
Abstract
Endurance athletes need a regular and well-detailed nutrition program in order to fill their energy stores before training/racing, to provide nutritional support that will allow them to endure the harsh conditions during training/race, and to provide effective recovery after training/racing. Since exercise-related gastrointestinal symptoms can significantly affect performance, they also need to develop strategies to address these issues. All these factors force endurance athletes to constantly seek a better nutritional strategy. Therefore, several new dietary approaches have gained interest among endurance athletes in recent decades. This review provides a current perspective to five popular diet approaches: (a) vegetarian diets, (b) high-fat diets, (c) intermittent fasting diets, (d) gluten-free diet, and (e) low fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAP) diets. We reviewed scientific studies published from 1983 to January 2021 investigating the impact of these popular diets on the endurance performance and health aspects of endurance athletes. We also discuss all the beneficial and harmful aspects of these diets, and offer key suggestions for endurance athletes to consider when following these diets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".